Sleep Stage Classification Using Advanced Intelligent Methods

  • José Manuel Sánchez Pascualvaca
  • Carlos Fernandes
  • Alberto Guillén
  • Antonio M. Mora
  • Rogerio Largo
  • Agostinho C. Rosa
  • Luis Javier Herrera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7902)


Manual sleep stage classification is a tedious process that takes a lot of time to sleep experts performing data analysis or studies on this field. Moreover errors and inconsistencies between classifications of the same data are frequent. Due to this, there is a great need of automatic classification systems to support reliable classification. This work extends the work by Herrera et al. (International Journal of Neural Systems 10.1142/S0129065713500123), inspecting the use of two techniques to improve the accuracy of sleep stage classifiers based on support vector machines from electroencephalogram, electrooculogram and electromyogram signals. Moreover, three different support vector machine multi-classifiers have been tested to evaluate and compare their performance. To accomplish these tasks, three different feature extraction techniques are applied to the electroencephalogram signals. First, the joint use of these feature sets, together with the electrooculogram and electromyogram information, is inspected (and compared with the use of each feature extraction method separately). Second the possibility of using nearby stages information to predict the current stage is inspected. Results obtained show significant improvements in the classification rates achieved using the two proposed techniques.


SVM Multi-classification EEG Feature Extraction Sleep stage classification 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • José Manuel Sánchez Pascualvaca
    • 1
  • Carlos Fernandes
    • 1
    • 2
  • Alberto Guillén
    • 1
  • Antonio M. Mora
    • 1
  • Rogerio Largo
    • 2
  • Agostinho C. Rosa
    • 2
  • Luis Javier Herrera
    • 1
  1. 1.Department of Computer Architecture and TechnologyUniversity of GranadaGranadaSpain
  2. 2.ISR-ISTLaseebLisbonPortugal

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